Standard techniques used in CAD for breast MRI are based on supervised artificial neural networks and have shown unsatisfactory discriminative results and limited application capabilities. The major disadvantages associated with these techniques are: (1) requirement of a fixed MR imaging protocol, (2) difficulties in diagnosing small breast masses with a diameter of only a few mm, (3) incapacity of capturing the lesion structure, and (4) training limitations due to an inhomogeneous lesions data pool. To overcome the above mentioned problems, the theme of this research plan becomes to employ biological neural networks which focus strictly on the observed complete MRI signal time-series, and enable a self-organized data-driven segmentation of dynamic contrast-enhanced breast MRI time-series w.r.t. fine-grained differences of signal amplitude, and dynamics, such as focal enhancement in patients with indeterminate breast lesions. The goal of the present project is to improve in an interdisciplinary framework the diagnostic quality in breast MRI. Specifically, the objectives of this proposed project are to: (1) develop, evaluate and test novel neural network techniques for functional and structural segmentation, visualization, and classification of dynamic contrast-enhanced breast MRI data, and thus, (2) substantially contribute to breast cancer diagnosis by improved further evaluation of suspicious lesions detected by conventional X-ray mammography. The PI is an electrical and computer engineer with a background in pattern recognition who has been developing new classification methods derived from the newest biological discoveries aiming to imitate decision-making, and sensory processing in biological systems. This Mentored Quantitative Research Career Development Award will permit the PI to acquire training in cancer research techniques and in computer assisted radiology, and to use these skills to extend and productively apply these new theoretical tools to biomedical applications. Accordingly, the long-term career goal of the PI is to become an effective researcher in the biomedical applications of pattern recognition, with specific emphasis in computer-aided diagnosis. The outcome of the proposed research is expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.